def bof_image_retrieval(self):
# load vocabulary and query feature
src = self.image_searcher()
q_descr, fp = self.load_query_feature()
# RANSAC model for homography fitting
model = homography.RansacModel()
rank = {}
# query
match_scores = [
w[0] for w in src.query(self.imlist[q_ind])[:nbr_results]
]
res_reg = [w[1] for w in src.query(self.imlist[q_ind])[:nbr_results]]
print('top matches:', res_reg)
self.plot_results(res_reg[:6], match_scores[:6])
if self.bof_rearrange:
# load image features for result
for ndx in res_reg[1:]:
locs, descr = sift.read_features_from_file(self.featlist[ndx])
# get matches
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
locs = np.array(locs)
tp = homography.make_homog(locs[:, :2].T)
# compute homography, count inliers.
try:
H, inliers = homography.H_from_ransac(fp[:, ind],
tp[:, ind2],
model,
match_theshold=4)
except:
inliers = []
# store inlier count
rank[ndx] = len(inliers)
# sort dictionary to get the most inliers first
sorted_rank = sorted(rank.items(),
key=lambda t: t[1],
reverse=True)
res_geom = [res_reg[0]] + [s[0] for s in sorted_rank]
# print('top matches (homography):', res_geom)
# show results
self.plot_results(res_geom[:6], match_scores[:6])
def H_homog_from_PCVSAC(kpts1_m, kpts2_m, xy_thresh_sqrd):
from PCV.geometry import homography
'Python Computer Visions Random Sample Consensus'
# Get xy points
xy1_m = kpts1_m[0:2,:]
xy2_m = kpts2_m[0:2,:]
# Homogonize points
fp = np.vstack([xy1_m, np.ones((1,xy1_m.shape[1]))])
tp = np.vstack([xy2_m, np.ones((1,xy2_m.shape[1]))])
# Get match threshold 10% of image diagonal
# Get RANSAC inliers
model = homography.RansacModel()
try:
H, pcv_inliers = homography.H_from_ransac(fp, tp, model, 500, np.sqrt(xy_thresh_sqrd))
except ValueError as ex:
printWARN('Warning 221 from H_homog_from_PCVSAC'+repr(ex))
return np.eye(3), []
# Convert to the format I'm expecting
inliers = np.zeros(kpts1_m.shape[1], dtype=bool)
inliers[pcv_inliers] = True
return H, inliers
# function to convert the matches to hom. points
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
# switch x and y - TODO this should move elsewhere
fp = vstack([fp[1], fp[0], fp[2]])
tp = vstack([tp[1], tp[0], tp[2]])
return fp, tp
# estimate the homographies
model = homography.RansacModel()
fp, tp = convert_points(1)
H_12 = homography.H_from_ransac(fp, tp, model)[0] # im 1 to 2
fp, tp = convert_points(0)
H_01 = homography.H_from_ransac(fp, tp, model)[0] # im 0 to 1
tp, fp = convert_points(2) # NB: reverse order
H_32 = homography.H_from_ransac(fp, tp, model)[0] # im 3 to 2
tp, fp = convert_points(3) # NB: reverse order
H_43 = homography.H_from_ransac(fp, tp, model)[0] # im 4 to 3
# warp the images
delta = 2000 # for padding and translation
